{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 蜡烛图形态\n",
    "**用TA_Lib寻找蜡烛图特征**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 目录\n",
    "1. 蜡烛图是什么？\n",
    "2. 蜡烛图的两大用途是什么？\n",
    "3. 6个常见蜡烛图是怎么计算的？\n",
    "4. 蜡烛图如何用图表显示\n",
    "5. 如何用TA_Lib寻找蜡烛图\n",
    "6. 多根蜡烛图组合（最常用的5种）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、 蜡烛图是什么？\n",
    "蜡烛图又称为K线图，主要包含四个数据，即开盘价、最高价、最低价、收盘价，所有的蜡烛图都是围绕这四个数据展开，反映大势的状况和价格信息。如果把每日的K线图放在一张图中，就能得到日K线图，同样也可画出周K线图、月K线图。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、 蜡烛图的两大用途是什么？\n",
    "1. 判断反转信号\n",
    "2. 判断持续信号"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3、 常见蜡烛图是怎么计算的？\n",
    "---\n",
    "**1. 大阳线: 涨幅大于5%，上下影线小于1.8%**\n",
    "\n",
    "$Close/Open>1.05$\n",
    "\n",
    "$High/Low < Close/Open+0.018$\n",
    "\n",
    "---\n",
    "\n",
    "**2. 大阴线： 跌幅大于5%， 上下影线小于1.8%**\n",
    "\n",
    "$Open/Close > 1.05$\n",
    "\n",
    "$High/Low < Open/Close+0.018$\n",
    "\n",
    "---\n",
    "\n",
    "**3.下影线**\n",
    "\n",
    "$(Min(Close,Open)-Low)/(High-Low)>0.667$\n",
    "\n",
    "---\n",
    "\n",
    "**4. 上影线**\n",
    "\n",
    "$(High-Max(Close,Open))/(High-Low)>0.667$\n",
    "\n",
    "---\n",
    "\n",
    "**5.十字星**\n",
    "\n",
    "$Close-Open<Abs(0.001)$\n",
    "\n",
    "$High-Low>0.001$\n",
    "\n",
    "**6. 缺口Gap**\n",
    "\n",
    "$open[-1]-close[-2]>0.01$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## http://www.stockstowatch.com/candle-indicator/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import talib as ta\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "stock1 = pd.read_excel('sz50.xlsx', sheetname='600036.XSHG', index_col='datetime')[-200:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4、 蜡烛图如何用图表显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FZoNQBwAAcjF0FcQ0PS0V752JrYJLuw8LJP1j/XliiebXeZ7UmoM9CCeJDFfduG5j/B5x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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18c2e36d208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pylab import date2num\n",
    "\n",
    "stock1['time'] = list(map(date2num, stock1.index))\n",
    "candle = stock1.reindex_axis([\"time\", \"open\", \"high\", \"low\", \"close\"], 1).values\n",
    "\n",
    "import matplotlib.finance as mpf\n",
    "\n",
    "fig, (ax) = plt.subplots(1,1, figsize=(15,12))\n",
    "fig.subplots_adjust(bottom=0.5)\n",
    "ax.grid(True)\n",
    "mpf.candlestick_ohlc(ax, candle, width=0.6, colorup='r', colordown='g',alpha=1.0)\n",
    "ax.xaxis_date ()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5、 如何用TA_Lib寻找蜡烛图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "CDLLONGLINE = ta.abstract.CDLLONGLINE(stock1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "axisX = CDLLONGLINE[CDLLONGLINE==100].index\n",
    "axisY = stock1.loc[axisX].low-0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def draw_candle(candle, scatterX, scatterY):\n",
    "    fig, (ax) = plt.subplots(1,1,sharex=True, figsize=(16,7))\n",
    "    ax.grid(True)\n",
    "    mpf.candlestick_ohlc(ax, candle, width=0.6, colorup='r', colordown='g',alpha=1.0)\n",
    "    ax.scatter(scatterX, scatterY, color='b', s=100, marker='^')\n",
    "    ax.xaxis_date ()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oDRX23M85pACAJAioAADMq1FXBPY8vfQXlvTZ/+XpkHxVVel5WNxzSEsHS6O9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\nDQL36CxxOSB6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18c31143898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_candle(candle, axisX, axisY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6、 多根蜡烛图组合\n",
    "1. Engulfing, bearish: 79% ta.CDLENGULFING\n",
    "2. Morning star: 78% ta.CDLMORNINGSTAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CDLENGULFING = ta.abstract.CDLENGULFING(stock1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CDLENGULFING_X = CDLENGULFING[CDLENGULFING==100].index\n",
    "CDLENGULFING_Y = stock1.loc[CDLENGULFING_X].low-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AxkVgBQBgjoXNUMu3LytshmpfaI+1HysAAL5gH1YAABZE93JX3cvdsocBAMDY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\nJ8l5BDvD6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18c2e0f8eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_candle(candle, CDLENGULFING_X, CDLENGULFING_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CDLMORNINGSTAR = ta.abstract.CDLMORNINGSTAR(stock1)\n",
    "CDLMORNINGSTAR_X = CDLMORNINGSTAR[CDLMORNINGSTAR==100].index\n",
    "CDLMORNINGSTAR_Y = stock1.loc[CDLMORNINGSTAR_X].low-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6uvztGZ6/+reTvDfJ20cPeXuS98zzGgAwl729+e4HAFoz7z6sf6+U8iVJXkzy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\nJLACAADQSf8/zJwK/VINNysAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18c2da7ada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_candle(candle, CDLMORNINGSTAR_X, CDLMORNINGSTAR_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
